class_docstr: An architecture combining hybrid Transformer/RNN with character-based
  inputs (token embeddings created via character-input CNN)
code: def add_args(parser):
      hybrid_transformer_rnn.HybridTransformerRNNModel.add_args(parser)
      parser.add_argument(
          "--char-embed-dim",
          type=int,
          default=128,
          metavar="N",
          help=("Character embedding dimension."),
      )
      parser.add_argument(
          "--char-cnn-params",
          type=str,
          metavar="EXPR",
          help=("String experission, [(dim, kernel_size), ...]."),
      )
      parser.add_argument(
          "--char-cnn-nonlinear-fn",
          type=str,
          default="tanh",
          metavar="EXPR",
          help=(
              "Nonlinearity applied to char conv outputs. Values: relu, tanh."
          ),
      )
      parser.add_argument(
          "--char-cnn-num-highway-layers",
          type=int,
          default=0,
          metavar="N",
          help=("Char cnn encoder highway layers."),
      )
      parser.add_argument(
          "--char-cnn-output-dim",
          type=int,
          default=-1,
          metavar="N",
          help="Output dim of the CNN layer. If set to -1, this is computed "
          "from char-cnn-params.",
      )
      parser.add_argument(
          "--use-pretrained-weights",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help="Use pretrained weights for the character model including "
          "the char embeddings, CNN filters, highway networks",
      )
      parser.add_argument(
          "--finetune-pretrained-weights",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help="Boolean flag to specify whether or not to update the "
          "pretrained weights as part of training",
      )
      parser.add_argument(
          "--pretrained-weights-file",
          type=str,
          default="",
          help=("Weights file for loading pretrained weights"),
      )
  
imports: import logging
  import math
  from ast import literal_eval
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture, transformer
  from fairseq.modules import SinusoidalPositionalEmbedding
  from pytorch_translate import char_encoder, char_source_model, hybrid_transformer_rnn, model_constants, rnn, transformer, utils, vocab_constants
  from pytorch_translate.common_layers import TransformerEncoderGivenEmbeddings, VariableTracker
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 2 for calling the CharSourceHybridModel obj's underlying member method code with example usage: obj.add_args(parser) without return types